



'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE__     :						int	=  logging.get_logger(__name__)


if is_vision_available():
			import PIL

class 							a__( snake_case__     ):
			a_	: int									=      ['''pixel_values''']






			def __init__(     self					,   _UpperCAmelCase = True					,   _UpperCAmelCase = None					,   _UpperCAmelCase = PILImageResampling.BICUBIC					,   _UpperCAmelCase = True					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   _UpperCAmelCase = 1 / 255					,   _UpperCAmelCase = True					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   **_UpperCAmelCase					,   )							->							None:
					super().__init__(**_UpperCAmelCase						)
					snake_case__	     =size if size is not None else {'shortest_edge': 224}
					snake_case__	     =get_size_dict(_UpperCAmelCase					,   default_to_square=_UpperCAmelCase						)
					snake_case__	     =crop_size if crop_size is not None else {'height': 224, 'width': 224}
					snake_case__	     =get_size_dict(_UpperCAmelCase					,   default_to_square=_UpperCAmelCase					,   param_name='crop_size'						)

					snake_case__	     =do_resize
					snake_case__	     =size
					snake_case__	     =resample
					snake_case__	     =do_center_crop
					snake_case__	     =crop_size
					snake_case__	     =do_rescale
					snake_case__	     =rescale_factor
					snake_case__	     =do_normalize
					snake_case__	     =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					snake_case__	     =image_std if image_std is not None else OPENAI_CLIP_STD
					snake_case__	     =do_convert_rgb






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = PILImageResampling.BICUBIC					,   _UpperCAmelCase = None					,   **_UpperCAmelCase					,   )							->							np.ndarray:
					snake_case__	     =get_size_dict(_UpperCAmelCase					,   default_to_square=_UpperCAmelCase						)
					if "shortest_edge" not in size:
							raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
					snake_case__	     =get_resize_output_image_size(_UpperCAmelCase					,   size=size['shortest_edge']					,   default_to_square=_UpperCAmelCase						)
					return resize(_UpperCAmelCase					,   size=_UpperCAmelCase					,   resample=_UpperCAmelCase					,   data_format=_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   **_UpperCAmelCase					,   )							->							np.ndarray:
					snake_case__	     =get_size_dict(_UpperCAmelCase						)
					if "height" not in size or "width" not in size:
							raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""						)
					return center_crop(_UpperCAmelCase					,   size=(size['height'], size['width'])					,   data_format=_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   **_UpperCAmelCase					,   )							->							Dict:
					return rescale(_UpperCAmelCase					,   scale=_UpperCAmelCase					,   data_format=_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   **_UpperCAmelCase					,   )							->							np.ndarray:
					return normalize(_UpperCAmelCase					,   mean=_UpperCAmelCase					,   std=_UpperCAmelCase					,   data_format=_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = ChannelDimension.FIRST					,   **_UpperCAmelCase					,   )							->							PIL.Image.Image:
					snake_case__	     =do_resize if do_resize is not None else self.do_resize
					snake_case__	     =size if size is not None else self.size
					snake_case__	     =get_size_dict(_UpperCAmelCase					,   param_name='size'					,   default_to_square=_UpperCAmelCase						)
					snake_case__	     =resample if resample is not None else self.resample
					snake_case__	     =do_center_crop if do_center_crop is not None else self.do_center_crop
					snake_case__	     =crop_size if crop_size is not None else self.crop_size
					snake_case__	     =get_size_dict(_UpperCAmelCase					,   param_name='crop_size'					,   default_to_square=_UpperCAmelCase						)
					snake_case__	     =do_rescale if do_rescale is not None else self.do_rescale
					snake_case__	     =rescale_factor if rescale_factor is not None else self.rescale_factor
					snake_case__	     =do_normalize if do_normalize is not None else self.do_normalize
					snake_case__	     =image_mean if image_mean is not None else self.image_mean
					snake_case__	     =image_std if image_std is not None else self.image_std
					snake_case__	     =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					snake_case__	     =make_list_of_images(_UpperCAmelCase						)

					if not valid_images(_UpperCAmelCase						):
							raise ValueError(
							    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
							    'torch.Tensor, tf.Tensor or jax.ndarray.'						)

					if do_resize and size is None:
							raise ValueError('Size must be specified if do_resize is True.'						)

					if do_center_crop and crop_size is None:
							raise ValueError('Crop size must be specified if do_center_crop is True.'						)

					if do_rescale and rescale_factor is None:
							raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

					if do_normalize and (image_mean is None or image_std is None):
							raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
							snake_case__	     =[convert_to_rgb(_UpperCAmelCase						) for image in images]

					# All transformations expect numpy arrays.
					snake_case__	     =[to_numpy_array(_UpperCAmelCase						) for image in images]

					if do_resize:
							snake_case__	     =[self.resize(image=_UpperCAmelCase					,   size=_UpperCAmelCase					,   resample=_UpperCAmelCase						) for image in images]

					if do_center_crop:
							snake_case__	     =[self.center_crop(image=_UpperCAmelCase					,   size=_UpperCAmelCase						) for image in images]

					if do_rescale:
							snake_case__	     =[self.rescale(image=_UpperCAmelCase					,   scale=_UpperCAmelCase						) for image in images]

					if do_normalize:
							snake_case__	     =[self.normalize(image=_UpperCAmelCase					,   mean=_UpperCAmelCase					,   std=_UpperCAmelCase						) for image in images]

					snake_case__	     =[to_channel_dimension_format(_UpperCAmelCase					,   _UpperCAmelCase						) for image in images]

					snake_case__	     ={'pixel_values': images}
					return BatchFeature(data=_UpperCAmelCase					,   tensor_type=_UpperCAmelCase						)









'''simple docstring'''

from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__     :						Tuple	=  {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE__     :						List[str]	=  [
			    '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''VanForImageClassification''',
			    '''VanModel''',
			    '''VanPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_van import (
						    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    VanForImageClassification,
						    VanModel,
						    VanPreTrainedModel,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)




